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Abstract:

Deformation of a substrate due to one or more processing steps is
determined by measuring substrate alignment data at lithographic
processing steps before and after the one or more processing steps. Any
abnormal pattern in the alignment data differential is identified by
comparing the calculated alignment data differential with previous data
accumulated in a database. By comparing the abnormal pattern with
previously identified tool-specific patterns for alignment data
differential, a processing step that introduces the abnormal pattern
and/or the nature of the abnormal processing can be identified, and
appropriate process control measures can be taken to rectify any anomaly
in the identified processing step.

Claims:

1. A method of controlling a manufacturing sequence including at least
one processing step, said method comprising: providing a process model
that correlates at least one mode of process variation within at least
one processing step with a pattern in a corresponding alignment data
differential between pre-processing alignment data and post-processing
data, wherein said pre-processing data is generated at a first alignment
step prior to said at least one processing step, and said post-processing
alignment data is generated at a second alignment step after said at
least one processing step; measuring first alignment data on a substrate
at said first alignment step; performing said at least one processing
step on said substrate after said first alignment step; measuring second
alignment data on said substrate at said second alignment step;
calculating an alignment data differential for said substrate as a
difference between said first alignment data and said second alignment
data; identifying a mode of process variation by matching a pattern in
said calculated alignment data differential for said substrate with said
process model; and altering operational procedure of a processing tool
associated with said identified mode of process variation based on a
predetermined processing tool operation protocol.

2. The method of claim 1, wherein a photoresist layer is present on said
substrate at said first alignment step.

3. The method of claim 1, wherein a photoresist layer is present on said
substrate at said second alignment step.

4. The method of claim 1, further comprising: storing said measured first
alignment data in an alignment database; storing said measured second
alignment data in said alignment database; and operating a computing
means to retrieve said measured first and second alignment data from said
alignment database and to calculate said alignment data differential.

5. The method of claim 1, further comprising determining whether said
calculated alignment data differential for said substrate is within a
predetermined limit for statistical variation therefor.

6. The method of claim 1, further comprising generating coefficients for
a series of polynomials that are orthogonal to one another on a shape of
said substrate, wherein said series of polynomials with corresponding
coefficients approximate said pattern in said calculated alignment data
differential.

7. The method of claim 6, further comprising generating coefficients for
at least another series of said polynomials that approximate each of at
least one mode of process variation, wherein said mode of process
variation is identified by selecting a mode of process variation that
provides greatest statistical correlation between corresponding
coefficients for same polynomials between said series of said at least
another series.

8. The method of claim 1, further comprising determining a new target for
a process parameter for said processing tool based on said calculated
alignment data differential and said process model.

9. The method of claim 1, wherein said predetermined processing tool
operation protocol is a statistical process control procedure that
employs input data generated from said calculated alignment data
differential and said process model.

10. The method of claim 9, wherein said input data is generated by a
computing means configured to run a program that performs said
predetermined processing tool operation protocol.

11. The method of claim 1, wherein said process model is generated
employing design or experiments (DOE) in which test substrates are
processed with different sets of test process parameters inducing
different degrees and/or modes of process variation among said at least
one mode of process variation.

12. The method of claim 11, wherein said process model is generated by
calculating correlation between each of said different sets of said test
process parameters and corresponding coefficients for a series of
polynomials that are orthogonal to one another and approximate an
alignment data differential in a corresponding test substrate.

13. The method of claim 1, wherein said at least one processing step is a
plurality of processing steps that are performed by a plurality of
processing tools, and said processing tool is selected from said
plurality of processing tools.

14. The method of claim 1, wherein said first alignment data and said
second alignment data include locations of dies on said substrate in two
orthogonal directions.

15. The method of claim 1, wherein said first alignment data and said
second alignment data include a change in lithographic image
magnification that is required to register a new image on preexisting
alignment marks on said substrate from standard magnification.

16. The method of claim 1, wherein said at least one processing step
includes at least one of a material deposition step, a material
conversion step, a dry etch step, a wet etch step, a planarization step,
an ion implantation step, and a bonding step.

17. A system for controlling a manufacturing sequence including at least
one processing step, said system comprising at least one processing tool,
at least one alignment tool, and at least one computing means, wherein
said at least one processing tool is configured to perform at least one
processing step, and said at least one computing means is configured to
perform the steps of: storing a process model that correlates at least
one mode of process variation within said at least one processing step
with a pattern in alignment data differential between pre-processing
alignment data and post-processing data, wherein said pre-processing data
is generated at a first alignment step prior to said at least one
processing step, and said post-processing alignment data is generated at
a second alignment step after said at least one processing step;
receiving first alignment data on a substrate that is measured by one of
said at least one alignment tool at said first alignment step; receiving
second alignment data on said substrate that is measured by said one or
another of said at least one alignment tool at said second alignment
step; calculating alignment data differential for said substrate by
subtracting said first alignment data from said second alignment data;
identifying a mode of process variation by matching a pattern in said
calculated alignment data differential for said substrate with said
process model; and generating instructions for altering operational
procedure of a processing tool among said at least one processing tool,
wherein said processing tool is associated with said identified mode of
process variation.

18. The system of claim 17, further comprising an alignment database
storing said measured first and said measured second alignment data and
in communication with said computing means to transmit said measured
first and second alignment data.

19. The system of claim 17, wherein said at least one computing means is
configured to perform a step of determining whether said calculated
alignment data differential for said substrate is within a predetermined
limit for statistical variation therefor.

20. The system of claim 17, wherein said at least one computing means is
configured to perform a step of generating coefficients for a series of
polynomials that are orthogonal to one another on a shape of said
substrate, wherein said series of polynomials with corresponding
coefficients approximate said pattern in said calculated alignment data
differential.

21. The system of claim 20, wherein said at least one computing means is
configured to perform a step of generating coefficients for at least
another series of said polynomials that approximate each of at least one
mode of process variation, wherein said mode of process variation is
identified by selecting a mode of process variation that provides
greatest statistical correlation between corresponding coefficients for
same polynomials between said series of said at least another series.

22. The system of claim 17, wherein said at least one computing means is
configured to perform a step of determining a new target for a process
parameter for said processing tool based on said calculated alignment
data differential and said process model.

23. The system of claim 17, wherein said at least one computing means
generates said instruction employing a predetermined processing tool
operation protocol encoded in said at least one computing means.

24. The system of claim 17, wherein said first alignment data and said
second alignment data include at least one data selected from locations
of dies on said substrate in two orthogonal directions and a change in
lithographic image magnification that is required to register a new image
on preexisting alignment marks on said substrate from standard
magnification.

25. The system of claim 17, wherein said at least one processing step
includes at least one of a material deposition step, a material
conversion step, a dry etch step, a wet etch step, a planarization step,
an ion implantation step, and a bonding step.

[0002] Various processing steps such lithographic exposure and
development, deposition, etching, and planarization are employed in
semiconductor manufacturing. Most processes that add material, such as
deposition, or remove material, such as etch and planarization, alter the
distribution of material on a substrate. The alteration in the
distribution of the material on the substrate causes structural changes
in the substrate by deforming the substrate.

[0003] Stress liners and stress-generating embedded elements are intended
to introduce stress into a substrate, which inevitably causes global
bowing of the substrate. In addition to such elements that are intended
to introduce stress, deposition, etch, or planarization of any material
on a substrate typically introduces some degree of deformation in the
substrate.

[0004] The pattern and the degree of deformation of a substrate depend on
the type of processing and the tool employed to effect the processing.
For example, low temperature chemical vapor deposition (LPCVD) tools tend
to have a thickness pattern in which the center region and regions in the
immediate vicinity of rail marks have a lesser thickness than the rest of
the substrate. Etch tools may have an inherent center-to-edge
nonuniformity in the amount of material removed from the substrate.
Chemical mechanical planarization (CMP) tools may have tool-specific
non-uniformity in the removal rate so that the remaining material on a
substrate tends to be thick or thin in a particular region relative to a
wafer notch or other global alignment features.

[0005] In order to maintain a high-yield stable manufacturing line,
process deviations in the various tools employed in a manufacturing line
need to be detected promptly, and any process deviations need to be
corrected as quickly as possible.

BRIEF SUMMARY

[0006] Deformation of a substrate due to one or more processing steps is
determined by measuring substrate alignment data at lithographic
processing steps before and after the one or more processing steps. Any
abnormal pattern in the alignment data differential is identified by
comparing the calculated alignment data differential with previous data
accumulated in a database. By comparing the abnormal pattern with
previously identified tool-specific patterns for alignment data
differential, a processing step that introduces the abnormal pattern
and/or the nature of the abnormal processing can be identified, and
appropriate process control measures can be taken to rectify any anomaly
in the identified processing step.

[0007] According to an aspect of the present disclosure, a method of
controlling a manufacturing sequence including at least one processing
step is provided. The method includes: generating a process model that
correlates at least one mode of process variation within at least one
processing step with a pattern in a corresponding alignment data
differential between pre-processing alignment data and post-processing
data, wherein the pre-processing data is generated at a first alignment
step prior to the at least one processing step, and the post-processing
alignment data is generated at a second alignment step after the at least
one processing step; measuring first alignment data on a substrate at the
first alignment step; performing the at least one processing step on the
substrate after the first alignment step; measuring second alignment data
on the substrate at the second alignment step; calculating an alignment
data differential for the substrate by subtracting the first alignment
data from the second alignment data; identifying a mode of process
variation by matching a pattern in the calculated alignment data
differential for the substrate with the process model; and altering
operational procedure of a processing tool associated with the identified
mode of process variation based on a predetermined processing tool
operation protocol.

[0008] According to another aspect of the present disclosure, a system for
controlling a manufacturing sequence is provided. The system includes at
least one processing step, the system including at least one processing
tool, at least one alignment tool, and at least one computing means. The
at least one processing tool is configured to perform at least one
processing step. The at least one computing means is configured to
perform the steps of: storing a process model that correlates at least
one mode of process variation within the at least one processing step
with a pattern in alignment data differential between pre-processing
alignment data and post-processing data, wherein the pre-processing data
is generated at a first alignment step prior to the at least one
processing step, and the post-processing alignment data is generated at a
second alignment step after the at least one processing step; receiving
first alignment data on a substrate that is measured by one of the at
least one alignment tool at the first alignment step; receiving second
alignment data on the substrate that is measured by the one or another of
the at least one alignment tool at the second alignment step; calculating
alignment data differential for the substrate by subtracting the first
alignment data from the second alignment data; identifying a mode of
process variation by matching a pattern in the calculated alignment data
differential for the substrate with the process model; and generating
instructions for altering operational procedure of a processing tool
among the at least one processing tool, wherein the processing tool is
associated with the identified mode of process variation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0009]FIG. 1 is flow chart that illustrates steps for an alignment data
based process control method according to an embodiment of the present
disclosure.

[0010]FIG. 2 is a flow chart that illustrates steps for generating a
process model for a manufacturing process according to an embodiment of
the present disclosure.

[0011]FIG. 3A is graph of the average X-magnification and the average
Y-magnification for wafers in a lot at a lithographic step for CA level
lithography.

[0012]FIG. 3B is graph of the average X-magnification and the average
Y-magnification for wafers in the lot at a lithographic step for CB level
lithography.

[0013]FIG. 3c is graph of the average X-magnification and the average
Y-magnification for wafers in the lot at a lithographic step for M3 level
lithography.

[0015]FIG. 5 is a wafer map illustrating, for each chip on a wafer, a
first vector representing the measured x displacement and the measured y
displacement of the alignment marks from their positions at a previous
alignment step and a second vector representing a corresponding vector
representing a mathematically approximated displacement vector pattern
generated by employing up to third degree polynomials.

[0016]FIG. 6 is an illustration of Zernike polynomials between the zeroth
degree and the third degree.

[0017] FIG. 7 is a wafer map illustrating, for each chip on the wafer, a
difference vector between the first vector and the second vector.

[0018]FIG. 8 is a schematic representation of a system for alignment data
based process control according to an embodiment of the present
disclosure.

DETAILED DESCRIPTION

[0019] As stated above, the present disclosure relates to methods of
controlling semiconductor manufacturing processes employing alignment
data generated during lithographic processing steps, which are now
described in detail with accompanying figures. Like and corresponding
elements mentioned herein and illustrated in the drawings are referred to
by like reference numerals. The drawings are not necessarily drawn to
scale.

[0020] Referring to FIG. 1, a flow chart illustrates steps for an
alignment data based process control method according to an embodiment of
the present disclosure. The alignment data based process control method
can be employed to control a manufacturing sequence including at least
one processing step, which can be a single non-lithographic processing
step or a plurality of non-lithographic processing steps.

[0021] Referring to step 100, a substrate is placed on an alignment tool
capable of aligning the substrate. The substrate can be a semiconductor
substrate, i.e., a substrate that includes at least one semiconductor
layer. The substrate can be a bulk semiconductor substrate or a
semiconductor-on-insulator (SOI) substrate as known in the art. In one
embodiment, the substrate can be a semiconductor wafer having a diameter
between 150 mm and 300 mm as known in the art. The substrate includes at
least one level of lithographic pattern so that the locations of the dies
therein can be measured in the alignment tool. The at least one level of
lithographic pattern includes all cumulative lithographic patterns up to
the time of the placement of the substrate in the alignment tool.

[0022] The alignment tool can be a unit in a lithographic tool as known in
the art. The lithographic tool may also include additional units for
applying a photoresist, lithographically exposing the photoresist, and/or
developing the photoresist. Alternately, the alignment tool may be a
standalone unit that is configured only to perform the operation of
measuring locations of alignment marks on the substrate.

[0023] Referring to step 110, first alignment metrology is performed on
the substrate. During the first alignment metrology, first alignment data
112 is generated by performing measurements on the substrate.
Specifically, the locations of a set of alignment marks discretely
distributed over a wafer are measured with respect to their expected
locations (the design locations represented in the wafer layout of the
particular mask containing the alignment marks) to comprise a set of mark
placement errors. From the set of placement errors, various parameters
that characterize the location and shape of each exposure field within
the substrate can be determined by the application of models that
continuously interpolate placement error among the discretely placed
alignment marks.

[0024] The alignment tool can generate the first alignment data 112 by
measuring locations of marks on the substrate in two different
directions. For example, the first alignment data 112 can include data on
a first translation distance of a mark along a first direction (such as
the x-direction) from a predefined reference point of the substrate, and
a second translation distance of a mark along a second direction (such as
the y-direction) from the predefined reference point of the substrate.
The first direction and the second direction are perpendicular to each
other. Thus, the alignment data is typically comprised of a set of
locations corresponding to two different perpendicular directions. A
common representation of such data is called a vector map, as shown in
FIG. 7, where the magnitude of the placement error in each direction
determine the length and orientation of each vector.

[0025] The first alignment data 112 can be modeled to determine parameters
that characterize the low order or "linear" modes of placement error
variation over the wafer; namely: translation, scale or magnification,
and rotation. The translation parameters (commonly referred to as "Tx"
and "Ty") are the average placement errors in the x- and y-directions.
The scale or magnification parameters (commonly referred to as "Mx" and
"My") are the degree of fractional stretching or shrinking of the
placement errors along the x- and y-directions. The rotation parameters
(commonly referred to as "Θx" and "Θy") are the degree of
fractional rotation of the placement errors along the x- and
y-directions. The translation (Tx, Tx) and average rotation,
Θavg=(Θx+Θy)/2, parameters typically do not correlate
strongly to process effects since they are dependent on the mechanical
positioning of the substrate with respect to the exposure tool. In most
cases, therefore, it is advisable to subtract Tx, Ty and Θavg
components from the alignment data.

[0026] Additionally, first alignment data 112 of sufficient density can be
modeled to determine parameters that characterize higher order or
"non-linear" modes of placement error variation over the wafer. Relative
to the linear modes, high order modes of placement error are indicative
of more localized deformations on the wafer.

[0027] Further, a polar coordinate system or any other coordinate system
can be employed instead of a Cartesian coordinate system. The magnitude
of the first translation distance and the second translation distance
may, or may not be, proportional to the distance of the center of each
die from the center of the substrate. Magnification in the x- and or
y-direction is indicative of bowing of the substrate due to cumulative
stress on the substrate at the time of the first alignment metrology. A
difference between the x- and y-direction rotation (commonly referred to
as "orthogonality") is indicative of the cumulative internal torsion of
the substrate due to non-uniformity in processing that has accumulated on
the substrate at the time of the first alignment metrology. Non-linear
modes of placement error as a function of the distance of the alignment
marks from the center of the substrate are indicative of non-uniformity
in processing on the substrate that has accumulated on the substrate at
the time of the first alignment metrology.

[0028] The first alignment data 112 can be electronically communicated to
a database, which is herein referred to as an alignment database 400. The
generation of the first alignment data 112 can be controlled by an
automated system that includes at least one computing means such as a
computer. The at least one communicating means is in communication with
the alignment database 400, and can be configured to control the
acquisition of the first alignment data 112 through measurements as well
as the uploading of the first alignment data 112 to the alignment
database 400.

[0029] Referring to step 120, if the alignment tool is a lithographic
alignment tool, i.e., an alignment tool that is a unit of a lithographic
system including lithographic exposure and development capability, the
first alignment metrology is a lithographic metrology, i.e., a
measurement step that is performed as part of a lithographic processing
step at which a photoresist layer on the substrate is exposed and
developed. In one embodiment, a photoresist layer may be present on the
substrate at step 110, either by application of the photoresist layer in
a prior processing step or by application of the photoresist layer in a
spin-coater in the lithographic system including the lithographic
alignment tool. In this case, the photoresist layer may be
lithographically exposed in an exposure unit within the lithographic
system at step 120. The application and lithographic exposure of the
photoresist layer is optional. In other words, the generation of the
first alignment data 112 may be performed with, or without, a photoresist
layer.

[0030] Referring to step 200, at least one processing step is performed on
the substrate. The at least one processing step can include at least one
non-lithographic processing step that introduces further deformation on
the substrate after the first alignment data is generated. The additional
deformation on the substrate can be introduced by addition of a new
material to the substrate, removal of a material from the substrate,
thermal processing that subjects the substrate to an elevated temperature
or a cryogenic temperature before returning to room temperature, or any
combination thereof. The addition of a new material can be performed by
deposition of a new material on the substrate, for example, by chemical
vapor deposition (CVD) or physical vapor deposition (PVD), by ion
implantation, and/or by plasma doping, or by conversion of an existing
material on the substrate into a new material, for example, by thermal
oxidation, thermal nitridation, plasma oxidation, and/or plasma
nitridation. Thermal processing that subjects the substrate to an
elevated temperature or a cryogenic temperature includes, but is not
limited to, a furnace anneal, a rapid thermal anneal (RTA), and cryogenic
clean.

[0031] The at least one processing step can be a single processing step
that is performed in a single processing tool, or can be a plurality of
processing steps that are performed in a plurality of processing tools.
Each of the at least one processing step can be any semiconductor
processing step such as a material deposition step, a material conversion
step, a dry etch step, a wet etch step, a planarization step, an ion
implantation step, and a bonding step.

[0032] Processing history data 212 can be generated at each processing
step among the at least one processing step of step 200, and can be
electronically communicated to the alignment database 400 or a different
database (not shown) that is in communication with the alignment database
400. The alignment database 400 can store the processing history data
212, which can include the list of processing tools employed to perform
each of the at least one processing step performed at step 200 of the
flow chart. The processing history data 212 can also include additional
data such as processing parameters employed at one or more of the at
least one processing step performed at step 200 of the flowchart.

[0033] Referring to step 300, the substrate is placed on an alignment tool
capable of aligning the substrate. This alignment tool may be the same
alignment tool employed for steps 100, 110, and optionally 120, or can be
a different alignment tool. The alignment tool can be a unit in a
lithographic tool as known in the art. The lithographic tool may also
include additional units for applying a photoresist, lithographically
exposing the photoresist, and/or developing the photoresist. Alternately,
the alignment tool may be a standalone unit that is configured only to
perform the operation of measuring locations of alignment marks on the
substrate.

[0034] Referring to step 310, second alignment metrology is performed on
the substrate. During the second alignment metrology, second alignment
data 312 is generated by performing measurements on the substrate.
Specifically, the locations of a set of alignment marks discretely
distributed over a wafer are measured with respect to their expected
locations (the design locations represented in the wafer layout of the
particular mask containing the alignment marks) to comprise a set of mark
placement errors. From the set of placement errors, various parameters
that characterize the location and shape of each exposure field within
the substrate can be determined by the application of models that
continuously interpolate placement error among the discretely placed
alignment marks.

[0035] The alignment tool generates second alignment data 312 that
includes at least the same type of data as the first alignment data 112.
Thus, if the first alignment data 112 includes data on locations of
alignment marks on the substrate in two different directions, the second
alignment data 312 includes data that is generated by measuring locations
of the alignment marks on the substrate in the two different directions.
If the first alignment data 112 can be modeled to determine the linear
components of placement error (translation, magnification and rotation),
the second alignment data can be modeled to determine the linear
components of placement error (translation, magnification and rotation).
If any other coordinate system is employed during generation of the first
alignment data 112, the corresponding coordinate system can be employed
during generation of the second alignment data 312 or during conversion
of the measured raw data into the second alignment data 312.

[0036] Any change in the first and second translation distances between
the first alignment data 112 and the second alignment data 312 can be
indicative of additional wafer deformation that is caused, for example,
by bowing, expansion or contraction, or torsion of the substrate due to
the at least one processing step of step 200. Magnification change
determined from the difference between the first and second alignment
data indicates a change in the bowing of the substrate caused by a change
in the stress on the substrate between the time of the first and second
alignment metrology that is attributable to least one processing step of
step 200. Orthogonality change determined from the difference between the
first and second alignment data indicates a change in the torsion of the
substrate between the time of the first and second alignment metrology
that is attributable to least one processing step of step 200. Changes to
higher order modes of placement error are indicative of non-uniformity in
processing attributable to least one processing step of step 200.

[0037] The second alignment data 312 can be electronically communicated to
the alignment database 400. The generation of the second alignment data
312 can be controlled by the automated system that includes at least one
computing means such as a computer. The at least one communicating means
is in communication with the alignment database 400, and can be
configured to control the acquisition of the second alignment data 312
during the measurements as well as the uploading of the second alignment
data 312 to the alignment database 400.

[0038] Referring to step 320, if the alignment tool is a lithographic
alignment tool, the second alignment metrology is a lithographic
metrology. In one embodiment, a photoresist layer may be present on the
substrate at step 310, either by application of the photoresist layer in
a prior processing step or by application of the photoresist layer in a
spin-coater in the lithographic system including the lithographic
alignment tool. In this case, the photoresist layer may be
lithographically exposed in an exposure unit within the lithographic
system at step 320. The application and lithographic exposure of the
photoresist layer is optional. In other words, the generation of the
second alignment data 312 may be performed with, or without, a
photoresist layer.

[0039] The alignment database 400 stores the first alignment data 112 and
the second alignment data 312. The alignment database 400 can also store
the processing history data 212.

[0040] Referring to step 410, alignment data differential is calculated
from the second alignment data 312 and the first alignment data 112 that
are stored in the alignment database. At least one computing means, such
as a computer or any other automated program in combination with hardware
configured to run the automated program, may be employed to retrieve the
measured first and second alignment data (112, 312) from the alignment
database 400 and to calculate the alignment data differential. In some
embodiments, the format of the measured first and second alignment data
(112, 312) can be in a form that enables generation of the calculated
alignment data differential by mathematical subtraction of the values for
the first alignment data 112 from the corresponding values for the second
alignment data 312. In other embodiments, the measured first and second
alignment data (112, 312) can be mathematically manipulated, for example,
by operating at least one computing means, to be converted into a format
that enables generation of the calculated alignment data differential by
mathematical subtraction of the values for the first alignment data 112
from the corresponding values for the second alignment data 312.

[0041] Referring to step 415, the calculated alignment data differential
is analyzed to determine whether any abnormal data is present in the
calculated alignment data differential, i.e., whether the calculated
alignment data differential is outside a predetermined limit for
statistical variation for the alignment data differential. The
predetermined limit may be automatically calculated by an algorithm that
runs on a computing means that performs statistical analysis on the data
that accumulates in the alignment database 400. In one embodiment, at
least one computing means can be employed, which is configured to
determine whether the calculated alignment data differential for the
substrate is within the predetermined limit for statistical variation
therefor. Alternately, the predetermined limit may be manually set based
on manufacturing needs, which can be established by correlating data on
yield and/or reliability of semiconductor chips previously manufactured
employing the same processes as the at least one processing step of step
200 or similar processing steps.

[0042] If there is no abnormal data in the calculated alignment data
differential, step 490 can be performed, at which no change is made to
the processing parameters for the process(es) among the at least one
processing steps of step 200. In other words, steps 100, 110, 120, 200,
300, 310, and 320 can be performed on subsequent substrates without
altering the process parameters for the process(es) among the at least
one processing steps of step 200.

[0043] If there is any abnormal data in the calculated alignment data
differential, step 425 is performed, at which a determination is made as
to whether the abnormality in the calculated alignment data differential
can be attributed to a specific processing step among the at least one
processing steps at step 200. This determination can be made by an
automated system that includes at least one computing means. The
determination can be made, for example, by performing mathematical
analysis on the calculated alignment data differential while utilizing
data from a process model 500.

[0044] If identification of a mode of process variation is not possible at
step 425, step 480 can be performed, at which wafer to wafer scanner
diagnostics may be performed to determine whether any of the alignment
tools employed at steps 110 and 310 is operating abnormally. A scanner
model 600 that characterizes the normal operation of the alignment tools
may be employed to compare whether the calculated alignment data
differential points to any abnormal operation of the alignment tools
employed at steps 110 and 3210 for the substrate.

[0045] If a mode of process variation is identified at step 425, step 430
is performed, at which a processing tool is indentified as the source of
the abnormality in the calculated alignment data differential. The data
from the process model 500 characterizes various modes of abnormality in
the alignment data differential based on unique signatures present in the
pattern of alignment data differential. Thus, by matching a pattern in
the calculated alignment data differential for the substrate as
calculated at step 410 with the process model, which includes various
in-substrate patterns for each of the various modes of abnormality in the
alignment data differential, a mode of process variation in a process
tool employed to perform one of the at least one processing steps at step
200 can be identified. If the at least one processing step at step 200 is
a plurality of processing steps that are performed by a plurality of
processing tools, the identified processing step as the cause of the
abnormality is one of the plurality of processing steps at step 200.
Correspondingly, the processing tool identified as the cause of the
abnormality in the calculated alignment data differential is selected
from the plurality of processing tools employed at step 200.

[0046] Referring to step 440, operational procedure of the processing tool
associated with the identified mode of process variation is altered,
i.e., modified, based on a predetermined processing tool operation
protocol. At least one computing means can be employed to generate the
instruction employing the predetermined processing tool operation
protocol, which can be encoded in the at least one computing means.

[0047] In one embodiment, the predetermined processing tool operation
protocol may require determination of a new target for a process
parameter for the identified processing tool based on the calculated
alignment data differential and the process model 500. For example, the
at least one computing means can be employed, which is configured to
perform the step of determining a new target value for a process
parameter for the processing tool based on the calculated alignment data
differential and the process model.

[0048] In another embodiment, the predetermined processing tool operation
protocol can be a statistical process control (SPC) procedure that
employs input data generated from the calculated alignment data
differential and the process model 500. Any known SPC procedures
compatible with the processing tools of step 200 can be employed as the
predetermined processing tool operational protocol. The input data may be
generated by a computing means configured to run a program that performs
the predetermined processing tool operation protocol.

[0049] Referring to step 200, the alteration to the operational procedure
determined at step 440 is applied to the applicable processing tool,
i.e., the processing tool associated with the identified mode of process
variation. This alteration is applied to the processing tool upon
determination of the alteration at step 440, for example, by electronic
communication to the processing tool and at least one computing means
that performs step 440. The alteration to the operational procedure is
applied to substrates to be subsequently processed in the at least one
processing tools of step 200.

[0050] Referring to FIG. 2, another flow chart illustrates steps for
generating a process model 500 for a manufacturing process according to
an embodiment of the present disclosure. The steps of this flow chart can
be employed for each processing step in the at least one processing step
of step 200 in FIG. 1 so that the process model 500 includes data for
various modes of abnormality for each of the processing steps at step 200
in FIG. 1.

[0051] The process model 500 correlates at least one mode of process
variation within at least one processing step with a pattern in a
corresponding alignment data differential between pre-processing
alignment data and post-processing data. The pre-processing data is
generated at a first alignment step prior to at least one processing
step. The first alignment step can be, for example, step 511. The
post-processing alignment data is generated at a second alignment step
after the at least one processing step. The second alignment step can be,
for example, step 531.

[0052] Referring to step 510, a set of test substrates is sequentially
placed on an alignment tool capable of aligning each test substrate. The
test substrates can be the same type as the substrate(s) employed in
manufacturing, e.g., the substrate of FIG. 1. The test substrates can be
bulk semiconductor substrates or semiconductor-on-insulator (SOI)
substrates as known in the art. Each test substrate includes at least one
level of lithographic pattern so that the locations of the dies therein
can be measured in the alignment tool. The alignment tool can be the same
as one of the alignment tools in FIG. 1.

[0053] Referring to step 511, first alignment metrology is performed on
the set of test substrates. During the first alignment metrology, first
test alignment data 518 is generated by performing measurements on the
substrate. Specifically, the same type of measurements is performed as
the measurements that generate the first alignment data 112 in FIG. 1.
Thus, the coordinates of the various corners of each die can be measured
to determine the location of a predetermined corner of the die, the
distortion of the die, and/or the deviation of the size of the die from
an ideal die size.

[0054] Specifically, the alignment tool generates first test alignment
data 518 that includes at least the same type of data as the first
alignment data 112. Thus, if the first alignment data 112 includes data
on locations of dies on the substrate in two different directions, the
first test alignment data 518 includes data that is generated by
measuring locations of the dies on the test substrates in the two
different directions. If the first alignment data 112 includes data on
rotations of predefined directions in dies, the first test alignment data
518 includes data that is generated by measuring rotations of the
predefined directions in the dies in the test substrates. If the first
alignment data 112 includes data on a change in lithographic image
magnification that is required to register a new image on preexisting
alignment marks on the substrate from standard magnification, the first
test alignment data 518 includes data that is generated by measuring the
same type of change in lithographic image magnification that is required
to print a new image on the test substrates or to register a new image on
preexisting alignment marks on the test substrates as measured from a
standard magnification. If any other coordinate system is employed during
generation of the first alignment data 112, the corresponding coordinate
system can be employed during generation of the first test alignment data
518 or during conversion of the measured raw data into the first test
alignment data 518.

[0055] The first test alignment data 518 can be electronically
communicated to a database, which is herein referred to as a design of
experiments (DOE) alignment database 540. The generation of the first
test alignment data 518 can be controlled by an automated system that
includes at least one computing means such as a computer. The at least
one communicating means is in communication with the DOE alignment
database 540, and can be configured to control the acquisition of the
first test alignment data 518 through measurements as well as the
uploading of the first test alignment data 518 to the DOE alignment
database 540.

[0056] Referring to step 512, lithographic exposure may be performed on
the set of test substrates as needed. Specifically, if a photoresist
layer is present at step 110 in FIG. 1 and if lithographic exposure is
performed on a substrate at step 120 in FIG. 1, a photoresist layer may
be provided at step 511 and is lithographically exposed at step 512. The
application and lithographic exposure of the photoresist layer is
optional, and depends on whether corresponding processes are performed on
the substrate in the flow chart of FIG. 1. In other words, the generation
of the first test alignment data 518 may be performed with, or without, a
photoresist layer depending on embodiments.

[0057] Referring to step 520, processing steps that are systematically
varied from a normal flow of at least one processing step are performed
on the set of test substrates. The set of at least one processing step
performed at step 520 is identical to the set of at least one processing
step performed at step 200 in FIG. 1 except that a variation is
introduced to one or more processing parameters in a processing step for
the test substrates at step 520. Specifically, during the processing of
each test substrate, at least one process parameter is set to a different
value on a processing step among the at least one processing step
employed in the flow chart of FIG. 1. Design of experiments (DOE) may be
employed to plan the selection of the different values for the at least
one process parameter during for the processing of the test substrates.
Thus, the test substrates are processed with different sets of test
process parameters inducing different degrees and/or modes of process
variation among the modes of process variation that each processing tool
employed at step 200 in FIG. 1 can have.

[0058] Referring to step 530, each test substrate is sequentially placed
on an alignment tool capable of aligning the test substrate. This
alignment tool may be the same alignment tool employed for steps 100 and
110, for steps 300 and 310, or for steps 510 and 511, or can be a
different alignment tool provided that the same type of alignment data
can be generated as in step 511.

[0059] Referring to step 531, second alignment metrology is performed on
the set of test substrates. During the second alignment metrology, second
test alignment data 538 is generated by performing measurements on the
set of test substrates. Specifically, the locations of a set of alignment
marks discretely distributed over each substrate are measured with
respect to their expected locations (the design locations represented in
the layout of the particular mask containing the alignment marks) to
comprise a set of mark placement errors. From the set of placement
errors, various parameters that characterize the location and shape of
each exposure field within the substrate can be determined by the
application of models that continuously interpolate placement error among
the discretely placed alignment marks.

[0060] The alignment tool generates second test alignment data 538 that
includes at least the same type of data as the first test alignment data
518. Thus, if the first test alignment data 518 includes data on
locations of the alignment marks on the substrate in two different
directions, the second test alignment data 538 includes data that is
generated by measuring locations of the alignment marks on the substrate
in the two different directions. If the first test alignment data 518
includes data on rotations of predefined directions in dies, the second
test alignment data 538 includes data that is generated by measuring, the
alignment tool can generate the second test alignment data 538 by
measuring rotations of the predefined directions in the dies. If the
first test alignment data 518 includes data on a change in lithographic
image magnification that is required to register a new image on
preexisting alignment marks on the substrate from standard magnification,
the second test alignment data 538 includes data that is generated by
measuring the same type of change in lithographic image magnification
that is required to register a new image on preexisting alignment marks
on the substrate from standard magnification. If any other coordinate
system is employed during generation of the first test alignment data
518, the corresponding coordinate system can be employed during
generation of the second test alignment data 538 or during conversion of
the measured raw data into the second test alignment data 538.

[0061] The change in the first and second translation distances between
the first test alignment data 518 and the second test alignment data 538
is indicative of additional wafer deformation that is caused, for
example, by bowing or local expansion or contraction of the substrate due
to the at least one processing step of step 520 including at least one
process variation.

[0062] Non-uniformity in the change of the first and second translation
distances can be indicative of non-uniformity in the bowing or local
expansion or contraction caused by the process(es) that is/are performed
at the at least one processing step of step 520. Magnification change
determined from the difference between the first 518 and second 538 test
alignment data indicates a change in the bowing of the substrate caused
by a change in the stress on the substrate between the time of the first
511 and second 531 test alignment metrology that is attributable to least
one processing step of step 520. Orthogonality change determined from the
difference between the first 518 and second 538 test alignment data
indicates a change in the torsion of the substrate between the first 511
and second 531 test alignment metrology that is attributable to least one
processing step of step 520. Changes to higher order modes of placement
error are indicative of non-uniformity in processing attributable to
least one processing step of step 520.

[0063] The second test alignment data 538 can be electronically
communicated to the DOE alignment database 540. The generation of the
second test alignment data 538 can be controlled by the automated system,
if present, that includes at least one computing means such as a
computer. The at least one communicating means is in communication with
the DOE alignment database 540, and can be configured to control the
acquisition of the second test alignment data 538 during the measurements
as well as the uploading of the second test alignment data 538 to the DOE
alignment database 540.

[0064] Referring to step 532, lithographic exposure may be performed on
the set of test substrates as needed. Specifically, if a photoresist
layer is present at step 310 in FIG. 1 and if lithographic exposure is
performed on a substrate at step 320 in FIG. 1, a photoresist layer may
be provided at step 531 and is lithographically exposed at step 532.

[0065] The first test alignment data 518 and the second test alignment
data 538 are stored in the DOE alignment database 540. Referring to step
541, the first test alignment data 518 and the second test alignment data
538 are subsequently utilized to calculate alignment data differential
for each test substrate. The pattern in the alignment data differential
in each test substrate can be analyzed to determine deformation
coefficients. The deformation coefficients characterize the various modes
of deformation that each variation in the process parameter at step 520
introduces on the set of test substrates. The deformation coefficients
can be calculated by employing any mathematical algorithm known in the
art such as least root mean square method. The calculation of the
deformation coefficients can be automated, for example, by employing a
computing means.

[0066] Referring to step 521, process parameters employed in the
systematically varied processing steps under design of experiments at
step 520 are extracted and transmitted to a computing means, which can be
a computer.

[0067] Referring to step 550, the process parameters employed in step 520
and the calculated deformation coefficients for the test substrates are
correlated. The correlation can be performed employing any mathematical
algorithm known in the art for determining correlation between multiple
variables and multiple measured datapoints.

[0068] Referring to step 599, the correlation can be employed to determine
a process model 500, which quantitatively correlates each identified
pattern of variations in the alignment data differential as calculated at
step 541 with a variation in a process parameter for the at least one
processing steps of step 520. In one embodiment, the process model 500 is
generated by calculating correlation between each of the different sets
of the test process parameters and corresponding coefficients for a
series of polynomials that are orthogonal to one another and approximate
an alignment data differential in a corresponding test substrate. As
discussed above, the at least one processing step of step 520 performs
the same set of at least one processing step of step 200 in FIG. 1.

[0069] Referring to FIGS. 3A-3C, the average X-magnification and the
average Y-magnification for wafers in a lot are shown at various
processing steps in an illustration of the changes in the measured
alignment data at various alignment steps.

[0070] The data in FIG. 3A is alignment data generated at a lithographic
step for CA level lithography. The CA level is a lithographic level that
prints gate contact via holes to gate conductors. The gate contact via
holes are formed in a contact-level dielectric material layer that is
deposited directly on the top surface of a semiconductor substrate. The
data in FIG. 3B is alignment data generated at a lithographic step for CB
level lithography. The CB level is a lithographic level that prints
substrate contact via holes to a semiconductor substrate. The substrate
contact via holes are formed in the contact-level dielectric material
layer. The wafer to wafer variation in the average X-magnification and
the average Y-magnification in FIG. 3A tracks the corresponding wafer to
wafer variation in the average X-magnification and the average
Y-magnification in FIG. 3B. In this case, only a CA level lithographic
exposure, an etch process for forming the gate contact via holes, and an
ashing process for a remaining portion of a photoresist layer after the
etch process are present between the CA level lithography and the CB
level lithography. Thus, the absence of signification change in the he
average X-magnification and the average Y-magnification for wafers
between FIG. 3A and FIG. 3B confirms that the processing steps between
the CA level lithography and the CB level lithography did not introduce
significant changes in substrate deformation.

[0071] However, the significant change in the average X-magnification and
the average Y-magnification is observed for the third wafer and the sixth
wafer between the CB level data shown in FIG. 3B and the M3 level data
shown in FIG. 3c. Thus, at least one of the processing steps between the
CB level lithography and the M3 level lithography introduced significant
changes in the average X-magnification and the average Y-magnification,
for example, through wafer bowing, expansion, and/or contraction. By
analyzing the pattern in the alignment data differential in the third
wafer and in the sixth wafer, as measured at the alignment step during
the CB lithography and at the alignment step during the M3 lithography,
tools that introduced the abnormal measured alignment data differential
in the third and sixth wafers can be identified.

[0072] Referring to FIG. 4, a graph illustrates a mechanism by which a
processing tool that introduces abnormal measured alignment data
differential can be identified. For example, if a stress-generating liner
deposition tool is known to provide a different degree of wafer bowing
(as schematically illustrated by three horizontal cross-sectional views
of a substrate under each datapoints), the process parameter can be
correlated with the measured differential grid magnification, i.e., the
change in the average X-magnification and/or in the average
Y-magnification. Specifically, the film stress can be indirectly
correlated to the differential grid magnification by first establishing a
linear relationship between backside wafer bow and grid magnification
change as illustrated in FIG. 4, and then by establishing a linear or
non-linear relationship between the wafer bow and film stress by
additional characterization of test substrates.

[0073] In some embodiments, other pattern dependent statistically-derived
quantities other than an average across a substrate can also be employed
to correlate measured alignment data differential with a process
parameter in a processing tool. Referring to FIG. 5, a wafer map
illustrates, for each chip on a wafer, a first vector and a second vector
(which are not distinguished in FIG. 5). Each first vector represents the
measured x displacement and the measured y displacement of the alignment
marks from their positions at a previous alignment step. Each second
vector represents a corresponding vector representing a mathematically
approximated displacement vector pattern generated by employing up to
third degree polynomials. Specifically, the second vectors are generated
using Zernike polynomials up to the third order. The magnitude of each
vector is scaled relative to a legend representing the distance
corresponding to 20 nm.

[0074] In one embodiment, the process model 500 can be generated by
calculating correlation between each of the different sets of the test
process parameters and corresponding coefficients for a series of
polynomials that are orthogonal to one another and approximate an
alignment data differential in a corresponding test substrate. Such
polynomials include, for example, Zernike polynomials, Bessel
polynomials, and any other set of orthogonal polynomials that are
orthogonal to one another on the shape of the substrate on which the at
least one processing steps of step 200 is performed. A set of polynomials
are orthogonal on the shape of an element if that set of polynomials is
defined only on the area of the shape of the element and each polynomial
within the set is orthogonal to all other polynomials within the set.

[0075] Referring to FIG. 6, an example is shown of polynomials that can be
employed as a set of orthogonal functions that can be used to approximate
the alignment data differential. Specifically, Zernike polynomials of the
zeroth order, the first order, the second order, and the third order are
shown from top to bottom.

[0076] Referring to FIG. 7, the difference vector between each pair of the
first vector and the second vector in the dies in FIG. 5 is plotted in a
wafer map.

[0077] In general, underlying mechanical distortion problems are governed
by a biharmonic equation, and a form of factored bounded biharmonic
functions are typically used in polar coordinates. Spherical harmonics in
the form of Legendre polynomials are also employed to express the angular
part. The radial part is often expressed through the Bessel functions.
For instance, circular membrane vibration modes are described in terms of
Bessel functions. If the base functions are appropriately chosen, the
coefficients for the orthogonal polynomials decay rapidly with an
increase in the order of the polynomials.

[0078] Thus, the calculated alignment data differential as generated at
step 410 in FIG. 1 can be approximated with a set of orthogonal
polynomials, and each calculated alignment data differential can be
characterized with a set of coefficients, which is herein referred to as
a set of deformation coefficients. Correspondingly, the determination of
whether any abnormal alignment data differential exists at step 415 in
FIG. 1 can be made by performing mathematical analysis on the set of
deformation coefficients derived from the calculated alignment data
differential generated at step 410 of FIG. 1. For example, the magnitude
of each deformation coefficients can be compared with a predefined range
for the corresponding deformation coefficient. If the magnitude of the
deformation coefficient is within the predefined range, the calculated
alignment data can be deemed normal, and if the magnitude of the
deformation coefficient is outside the predefined range, the calculated
alignment data can be deemed abnormal.

[0079] Further, the determination on whether any observed abnormality in
the calculated alignment data differential can be attributable to any
processing step at step 425 of FIG. 1 can be made by performing
mathematical analysis on the set of deformation coefficients derived from
the calculated alignment data differential generated at step 410 of FIG.
1. For example, the pattern in the calculated deformation coefficients
can be compared with multiple predefined patterns representing a mode of
process variation that occurs in the processing tools employed at step
200. The predefined patterns can be encoded in the process model 500. If
the pattern in the calculated deformation coefficients matches one of the
patterns for process variations as encoded in the process model, the
cause of the observed abnormal differential data differential can be
attributed to the corresponding process tool, and an appropriate
adjustment can be made to the operational procedure for the process tool
at step 440 in FIG. 1.

[0080] Further, the determination of deformation coefficients as performed
at step 541 in FIG. 2 can employ the same set of orthogonal polynomials.
Thus, the process model 500 can be encoded employing various sets of
deformation coefficients representing the various modes of process
variations that occur in the processing tools employed in step 520 of
FIG. 2, which are the same processing tools employed in step 200 in FIG.
1.

[0081] In general, at step 410 in FIG. 1, coefficients are generated for a
series of polynomials that are orthogonal to one another on the shape of
the substrate or the test substrates. The series of polynomials, with
corresponding coefficients, approximate the pattern in the calculated
alignment data differential. In one embodiment, at least one computing
means is configured to perform these steps.

[0082] Further, at step 541 in FIG. 2, coefficients for at least another
series of the polynomials are generated. The at least another series of
polynomials, with corresponding coefficients, approximate each of at
least one mode of process variation introduced under the design of
experiments at step 520. In this case, at step 430, the mode of process
variation can be identified by selecting a mode of process variation that
provides greatest statistical correlation between corresponding
coefficients for same polynomials between the series of the at least
another series. In one embodiment, at least one computing means is
configured to perform these steps.

[0083]FIG. 8 illustrates a system for alignment data based process
control according to an embodiment of the present disclosure. The system
can be employed to control controlling a manufacturing sequence by
directing a hardware flow (i.e., flow of substrates) and data flow. The
system includes at least one processing step, which is performed by at
least one processing tool. The at least one processing tool are
represented as a first processing tool 822, a second processing tool 824,
and a third processing tool 826. Each of the at least one processing tool
(822, 824, 826) is configured to perform at least one processing step,
which correspond to the at least one processing step of step 200 in FIG.
1 or step 520 in FIG. 2. The system also includes at least one computing
means 910, which are represented as a computer including at least one
memory device. The memory device can be, but is not limited to, a hard
disk, a USB drive, a tape drive, and a disk writing device 940 configured
to store data on a disk 942. For example, the disk writing device 940 can
be a CD ROM writing drive or a DVD ROM writing device, and the disk can
be a CD ROM disk or a DVD ROM disk.

[0084] A data storage device 860 is also provided, which can be a
standalone device or a device incorporated into the at least one
computing means 910. If the data storage device 860 is a standalone
device, the data storage device 860 is in electronic communication with
the at least one computing means 910 via a data cable 930 or via wireless
communication.

[0085] The system further includes at least one alignment tool, which is
represented as a first alignment tool 810 that can perform steps 100,
110, and 120 in FIG. 1 and/or steps 510, 511, 512 in FIG. 2 and a second
alignment tool 820 that can perform steps 300, 310, and 320 in FIG. 1
and/or steps 530, 531, and 532 in FIG. 2. The first alignment tool 810
and the second alignment tool 820 can be different tools or the same
tool. The hardware, e.g., the substrate in FIG. 1 or the set of test
substrates in FIG. 2, is directed along the direction of the block
arrows, i.e., sequentially to the first alignment tool 810, to the first
processing tool 822, to the second processing tool 824, to the third
processing tool 826, to the second alignment tool 820, and subsequent
processing tools (not shown). The data and control instructions flow in
the direction of the arrow and between the at least one computing means
910 and the data storage device 860, which can function as the hardware
performing the function of the alignment database 400 and/or the function
of the DOE alignment database 540.

[0086] The at least one computing means 910 can be configured to perform
the steps of: [0087] (a) storing a process model that correlates at
least one mode of process variation within the at least one processing
step of the at least one processing tool (822, 824, 826) with a pattern
in alignment data differential between pre-processing alignment data and
post-processing data, wherein the pre-processing data is generated at a
first lithographic alignment step prior to the at least one processing
step employing the first alignment tool 810, and the post-processing
alignment data is generated at a second lithographic alignment step after
the at least one processing step employing the second alignment tool 820;
[0088] (b) receiving first alignment data on a substrate that is measured
by one of the at least one lithographic alignment tool, e.g., the first
alignment tool 810, at the first lithographic alignment step; [0089] (c)
receiving second alignment data on the substrate that is measured by the
one or another of the at least one lithographic alignment tool, e.g., the
second alignment tool 820, at the second lithographic alignment step;
[0090] (d) calculating alignment data differential for the substrate by
subtracting the first alignment data from the second alignment data;
[0091] (e) identifying a mode of process variation by matching a pattern
in the calculated alignment data differential for the substrate with the
process model; and [0092] (f) generating instructions for altering
operational procedure of a processing tool among the at least one
processing tool, wherein the processing tool is associated with the
identified mode of process variation.

[0093] The at least one computing means 910 houses a processor, memory and
other systems components (not shown expressly in the drawing) that
implement a general purpose processing system, or computer that may
execute a computer program product. The computer program product may
comprise media, for example a compact storage medium such as a compact
disc, which may be read by the processing unit through a disc drive, or
by any means known to the skilled artisan for providing the computer
program product to the general purpose processing system for execution
thereby.

[0094] The computer program product may comprise all the respective
features enabling the implementation of the inventive method described
herein, and which--when loaded in a computer system--is able to carry out
the method. Computer program, software program, program, or software, in
the present context means any expression, in any language, code or
notation, of a set of instructions intended to cause a system having an
information processing capability to perform a particular function either
directly or after either or both of the following: (a) conversion to
another language, code or notation; and/or (b) reproduction in a
different material form.

[0095] The computer program product may be stored on hard disk drives
within processing unit, as mentioned, or may be located on a remote
system such as a server (not shown), coupled to the processing unit, via
a network interface such as an Ethernet interface. A monitor, a mouse, a
keyboard, and any other human interface device can be coupled to the
processing unit, to provide user interaction. A scanner (not shown)
and/or a printer (not shown) may be provided for document input and
output.

[0096] While the disclosure has been described in terms of specific
embodiments, it is evident in view of the foregoing description that
numerous alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, the disclosure is intended to
encompass all such alternatives, modifications and variations which fall
within the scope and spirit of the disclosure and the following claims.